جست‌وجوی استاد و پژوهشگر

Emre Salmanمشاهده پروفایل

استاد

• Integrated Circuits (ICs)
• VLSI Systems
• Energy-Efficient Computing
• +۵ مورد دیگر

Emre Salman is a Professor in the Department of Electrical and Computer Engineering at Stony Brook University (SUNY), where he directs the Nanoscale Circuits and Systems (NanoCAS) Lab. His research focuses on nanoscale IC design, energy-efficient computing, and biomedical electronics, with notable contributions to 3D integrated circuits and wireless energy harvesting for IoT and healthcare applications. Education : PhD in Electrical Engineering, University of Rochester (2009) MSc in Electrical and Computer Engineering, University of Rochester (2006) BSc in Microelectronics Engineering, Sabanci University, Turkey (2004) Research Interests : Salman’s work spans energy-efficient integrated circuits, secure IoT systems, and implantable medical devices. He pioneers techniques like charge-recycling logic and thermal-aware design for post-Moore computing. His group develops monolithic 3D ICs to address power/thermal challenges in AI accelerators and biomedical implants. Articles Trends : Recent publications highlight advancements in triboelectric energy harvesters for knee implants, thermal covert channel mitigation in 3D processors, and energy-efficient DNN accelerators. He emphasizes sustainability and security in emerging technologies like ReRAM-based computing and AC circuits for wireless IoT. Awards : 2023-2024 IEEE Distinguished Lecturer 2018 IEEE Region 1 Technological Innovation Award 2013 NSF CAREER Award Advising & Grants : Salman has directed multiple NSF, NIH, and industry-funded projects. He advises students on topics like hardware security and biomedical electronics, with a focus on translating research into commercializable technologies. Labs & Teams : The NanoCAS Lab collaborates with Brookhaven National Lab and industry partners (e.g., AMD, Samsung) to bridge academic research with real-world applications in energy-efficient computing and secure 3D ICs.

Ahmet Bindalمشاهده پروفایل

استاد

• Nano-scale Electron Devices
• Silicon Nanowire Transistors
• VLSI Architecture
• +۴ مورد دیگر

Professor Ahmet Bindal is a faculty member in the Department of Computer Engineering at San José State University . He earned his B.S. in Electrical Engineering from Bogazici University, Turkey, followed by M.S. and Ph.D. degrees from the University of California, Los Angeles. Industry Experience : 20 years at IBM, Intel, Philips, and Cadence Design Systems. Current Research : Nano-scale electron devices, silicon nanowire transistors, robotics, and VLSI architecture. Research Trends : His work focuses on silicon nanowire transistors for VLSI, FPGA, and robotics. Key themes include low-power/high-speed integrated circuits , dynamic logic design , neuromorphic engineering , and advanced semiconductor processing . Patents and Publications : He holds four U.S. patents (three with IBM, one with Intel). His 30+ journal and conference publications span nanowire transistors, FPGA architecture, robotics, and semiconductor process modeling. Teaching Contributions : Developed an undergraduate System-on-Chip (SoC) course and a MOSFET design laboratory at SJSU. Books Authored : Fundamentals of Computer Architecture and Design (Springer, 2017). Electronics for Embedded Systems (Springer, 2017). Silicon Nanowire Transistors (Springer, 2017).

• FPGA Design
• VLSI Systems
• Logic Synthesis
• +۷ مورد دیگر

Pierre-Emmanuel Gaillardon is a Professor in the Department of Electrical & Computer Engineering and Adjunct Professor in the School of Computing at the University of Utah. He holds a joint appointment since July 2024, having previously served as Assistant Professor (2016–2019) and Adjunct Assistant Professor in Computing (2016–2019). His research focuses on FPGA design, VLSI systems, nanoelectronics, and hardware security. He leads projects in emerging devices like TIGFETs, compute-in-memory architectures, and radiation-hardened FPGA fabrics. Teaching includes courses on Digital VLSI Design, Embedded Systems Design, and thesis supervision. He has secured grants from NSF, DARPA, and industry partners totaling over $10M, addressing topics like FPGA redaction, neuromorphic systems, and environmental sensors. Notable awards include the NSF CAREER Award (2018) and IEEE Senior Member elevation (2016). He actively serves on IEEE committees for nanoelectronics and EDA tools, contributing to standards like OpenFPGA.

• Digital Circuits and Systems
• Energy Efficiency
• Smart Cities
• +۱۳ مورد دیگر

Enrico Macii is a Full Professor at the Politecnico di Torino, affiliated with the Interuniversity Department of Regional and Urban Studies and Planning (DIST) and the Department of Control and Computer Engineering (DAUIN). He leads the Electronic Design Automation (EDA) research group and holds key roles as Scientific Advisor for the Politecnico-STMicroelectronics partnership and Scientific Contact for the European Chips Joint Undertaking. Research Interests: His work spans digital circuits and systems, energy efficiency, smart cities, Industry 4.0, and smart manufacturing. He focuses on embedded and cyber-physical systems, low-power design, neuromorphic computing, AIoT, and sustainable urban development. Recent Publications: His recent research demonstrates strong trends in edge AI, neuromorphic computing, and smart energy systems. Articles highlight innovations in low-power hardware acceleration, federated learning, physics-informed AI, and digital twin applications for urban and industrial systems. There is a clear emphasis on deploying AI efficiently on constrained devices and integrating physical models with machine learning. J. William Fullbright Fellowship (1993) Best paper award IEEE European Design Automation Conference (1996) Best paper award ACM/IEEE Great Lakes Symposium on VLSI (2008) DAC Service Award (2014) IEEE Fellow (2006) DATE Fellow (2014) Advising and Grants: He has supervised over 25 PhD students in computer engineering, AI, and urban systems. His research is funded by major EU programs (Horizon 2020, PNRR, KDT JU), national (PRIN, FAR), and regional grants, as well as industrial contracts with STMicroelectronics, Michelin, and Cefriel. He leads numerous high-impact projects in smart manufacturing, energy efficiency, and digital twins. Labs and Teams: He is a core member of the EDA Group, an interdepartmental research team at Politecnico di Torino focusing on VLSI-CAD, bioinformatics, smart cities, and Industry 4.0. He also contributes to IAM@PoliTo (Integrated Additive Manufacturing) and leads multiple EU and national research consortia.

• AI Hardware
• Digital Chip Design
• Efficient AI Algorithms
• +۶ مورد دیگر

Deliang Fan is an Associate Professor at the School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ. His research focuses on AI hardware, in-memory computing, and neuromorphic systems. He received his MS and PhD from Purdue University under Prof. Kaushik Roy. Education: PhD, Purdue University (2015) Research Interests: AI Hardware, In-Memory Computing, Adversarial AI, Neuromorphic Computing His work spans cross-layer co-design for AI applications, including deep learning, bioinformatics, and graph processing. He has authored 170+ peer-reviewed papers and developed hardware solutions for spintronic and memristor-based systems. Recent publications emphasize efficient architectures for transformers, federated learning, and robust neural networks. Awards include the NSF Career Award and multiple best paper recognitions. He serves in editorial and organizational roles for leading conferences like DAC, ISQED, and GLSVLSI.

• VLSI CAD
• Low-power IC Design
• Mixed-signal IC Design
• +۵ مورد دیگر

Riadul Islam serves as an Assistant Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC), maintaining his primary office in room 316 of the Information Technology and Engineering (ITE) Building. His academic appointment focuses on hardware design and verification within the institution's engineering framework. His educational qualifications include: Ph.D. in Computer Engineering from UCSC (2017) M.A.Sc. in Electrical and Computer Engineering from Concordia University, Montreal (2011) B.Sc. in Electrical and Electronic Engineering from Bangladesh University of Engineering and Technology (2007) Professor Islam's research centers on VLSI CAD tools and low-power digital/mixed-signal IC design , with significant contributions to current-mode clock networks, vehicular security systems, and error-robust circuit architectures. His work increasingly integrates machine learning for design automation while exploring neuromorphic computing applications and secure hardware implementations. This multidisciplinary approach bridges traditional IC design with modern AI-driven optimization techniques. Analysis of his 2023-2025 publications reveals three dominant research thrusts: (1) Machine learning applications in early-stage Design Rule Checking (DRC) prediction and clock network optimization, (2) Graph-based intrusion detection systems for automotive networks (particularly CAN bus security), and (3) Event-based vision systems and neuromorphic computing architectures. These areas demonstrate consistent innovation in merging hardware design with AI/ML methodologies for enhanced system reliability and efficiency. He directs the UMBC VLSI and SoC Research Group , which develops energy-efficient clocking networks, secure vehicular communication protocols, and compute-in-memory architectures. The lab maintains active collaboration with industry partners on hardware security and neuromorphic computing initiatives while supporting graduate student research in cutting-edge IC design methodologies.

• In-Memory Computing
• Energy-Efficient Systems
• Machine Learning Accelerators
• +۳ مورد دیگر

Prof. Naresh R. Shanbhag holds the Jack S. Kilby Professorship in Electrical and Computer Engineering at the University of Illinois Urbana-Champaign. He is affiliated with the Coordinated Science Lab and the Information Trust Institute, both part of the College of Engineering. His research focuses on energy-efficient computing architectures, in-memory computing, nanoelectronics, and secure machine learning systems. Prof. Shanbhag has pioneered work on statistical error compensation techniques and resistive crossbar-based architectures, addressing critical challenges in low-power, high-performance computing. Education and career details are not explicitly provided in the text, but his extensive publications (313+) and honors indicate deep academic involvement. His research interests emphasize the intersection of hardware architecture and machine learning, with a focus on emerging technologies like MRAM and SRAM-based in-memory computing. Key awards include the IEEE Fellow designation (2006) and the prestigious Semiconductor Industry Association (SIA) University Research Award (2018). His recent work explores security vulnerabilities in in-memory architectures, energy-accuracy trade-offs in resistive systems, and adversarial robustness of neural networks. Prof. Shanbhag’s contributions span over 20 years, bridging theoretical computer science with practical hardware implementations. His lab (Coordinated Science Lab) and collaborations drive innovations in nanoscale information processing and trustworthy computing systems.

Ahmedullah Azizمشاهده پروفایل

استادیار

• Mixed-Signal VLSI Circuits
• Non-Volatile Memory
• Beyond CMOS Device Design
• +۴ مورد دیگر

Ahmedullah Aziz is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville, within the Tickle College of Engineering. He earned his Ph.D. in Electrical and Computer Engineering from Purdue University (2019), an M.S. from Pennsylvania State University (2016), and a B.S. from Bangladesh University of Engineering & Technology (2013). His research focuses on mixed-signal VLSI circuits, non-volatile memory, and beyond-CMOS device design, emphasizing emerging technologies like ferroelectrics and spintronics. He leads the NorDIC Lab and has published over 60 articles in journals, conferences, and patents. Education: PhD, Electrical & Computer Engineering, Purdue University, 2019 MS, Electrical Engineering, Pennsylvania State University, 2016 BS, Electrical & Electronic Engineering, BUET, 2013 Research Interests: Aziz explores device-circuit-system co-design techniques, particularly in cryogenic neuromorphic systems, superconducting memory, and ferroelectric-based circuits. His work bridges material innovation with practical applications, aiming to advance energy-efficient and high-performance computing. Awards: EDAA Outstanding Dissertation Award (2019/2020) Outstanding Graduate Student Research Award (Purdue, 2019) Samsung 'Icon' Award (2013) Best Publication Awards (SRC-DARPA STARnet, 2015/2016) Advising & Grants: While specific grants are not detailed, his research has been supported by prestigious institutions. He has advised/co-advised projects in emerging technologies but no listed students. His work extends to reviewing IEEE journals, conference TPC roles, and editorial contributions. Labs & Teams: He directs the NorDIC Lab, focusing on next-generation computing and device innovation.

• CMOS integrated circuits
• Computer architecture
• Electrical and electronic engineering
• +۱۲ مورد دیگر

Maurizio Zamboni is a Full Professor at the Department of Electronics and Telecommunications (DET) at the Polytechnic University of Turin, where he also serves as Student Ombudsman. His academic career spans over three decades with continuous teaching and research contributions in electronics and computing fields. Professor Zamboni's research interests focus on cutting-edge areas including CMOS integrated circuits, computer architecture, quantum computing, semiconductor devices, and VLSI design. His work particularly emphasizes emerging nanotechnologies for digital microelectronic architectures and the design of high-performance or low-consumption processing systems. He has developed expertise in circuit architectures for probabilistic computing, logic-in-memory computing, magnetic devices, and quantum architectures. His recent publications (2021-2025) reveal a strong trend toward quantum computing applications, in-memory processing architectures, and novel approaches to overcoming the memory wall problem. These works span both theoretical algorithm development and practical hardware implementations, with significant focus on quantum annealing, FPGA-based quantum emulation, and memory-mapped processing architectures. Professor Zamboni has been actively supervising PhD students working on quantum computing algorithms, hardware AI accelerators for automotive applications, and quantum-related optimization approaches. He leads research within the VLSILAB Group at DET, focusing on the intersection of nanoelectronics, quantum computing, and advanced computer architectures. His work bridges theoretical computer science with practical electronic design, creating novel solutions for next-generation computing challenges.

Subramanian Iyerمشاهده پروفایل

استاد

• System Scaling Technology
• Advanced Packaging
• 3D Integration
• +۲ مورد دیگر

Subramanian Iyer is a Distinguished Professor at the University of California, Los Angeles (UCLA), holding the Charles P. Reames Endowed Chair in Electrical Engineering with joint appointments in Electrical and Computer Engineering and Materials Science and Engineering. His laboratory focuses on cutting-edge semiconductor research. Primary research domains include: System Scaling Technology : Innovations in semiconductor miniaturization 3D Integration & Advanced Packaging : Techniques for vertical chip stacking and heterogeneous integration Memory Subsystems : Development of embedded DRAM and novel memory architectures Neuromorphic Computing : Hardware implementations of brain-inspired computing Publication analysis reveals three dominant themes across 35+ years of research: Semiconductor materials innovation (SiGe alloys, silicides, MBE growth) Memory technology evolution (embedded DRAM, eFUSE, low-latency designs) System integration advancements (3D packaging, TSV, heterogeneous integration) Significant honors include: IEEE Fellow (1995) and Daniel Noble Award (2012) National Academy of Inventors Fellow (2017) IBM Fellow (2010) with 30+ invention plateaus IMAPS Educator Award (2021) and Daniel C. Hughes Memorial Award (2020) He leads UCLA's NanoLab facility and has been featured in The New York Times and Wall Street Journal for pioneering chiplet technology. Current work focuses on megachip architectures and open-access semiconductor prototyping.

Linda Katehiمشاهده پروفایل

استاد

• Microwave and Millimeter-Wave Circuits
• MEMS Devices
• Terahertz Applications
• +۵ مورد دیگر

Linda Katehi is a Professor of Electrical & Computer Engineering and Materials Science & Engineering at Texas A&M University, holding the O'Donnell Foundation Chair II. She is a Member of the National Academy of Engineering and American Academy of Arts and Sciences. Her research focuses on advanced electromagnetic systems, MEMS devices, and embedded intelligent sensors. Katehi earned her Ph.D. in Electrical Engineering from UCLA (1984), with prior degrees from UCLA and the National Technical University of Athens. Education: Ph.D., Electrical Engineering, UCLA (1984) M.S., Electrical Engineering, UCLA (1981) B.S., Electrical and Mechanical Engineering, National Technical University of Athens (1977) Research Interests: Katehi pioneers innovations in microwave circuits, MEMS-based reconfigurable systems, terahertz technology, and neuromorphic sensors. Her work emphasizes integrating artificial intelligence into hardware for adaptive sensing platforms. Recent projects include flexible electronics, time-domain system analysis, and sustainability in urban infrastructure. Awards & Recognition: Ramo Simon Founder’s Award (2015) Charter Fellow, National Academy of Inventors (2013) Leading Women in STEM Award (2012) Rudy E. Henning Mentoring Award (IEEE, 2011) Lab & Teams: Directs the Intelligent Electromagnetic Sensors Lab (IEMSL), advancing embodied intelligence in electronics. Her team develops AI-embedded sensors and reconfigurable systems for applications in healthcare, communications, and environmental monitoring. Collaborates across disciplines to address global sustainability and equity challenges.

Mehdi Sadiمشاهده پروفایل

استادیار

• Secure and Reliable System-on-Chip Design
• AI and Neuromorphic Hardware
• Safety-critical Hardware
• +۲ مورد دیگر

Mehdi Sadi is an Assistant Professor of Electrical and Computer Engineering at Auburn University's College of Engineering. He holds a Ph.D. from the University of Florida, an M.S. from the University of California-Riverside, and a B.S. from Bangladesh University of Engineering and Technology. His research focuses on secure and reliable system-on-chip design, AI/ML-driven VLSI CAD/EDA, neuromorphic hardware, and emerging post-CMOS computing technologies. Notable achievements include earning the NSF CAREER Award for chiplet-based design optimization and a $175k NSF grant for magnetic RAM research. His work integrates machine learning with hardware co-design to enhance AI accelerators' performance, energy efficiency, and security. Recent projects include adversarial attack mitigation on AI hardware and reliability analysis of neuromorphic systems. Dr. Sadi's contributions span chiplet architecture, memory systems (e.g., STT-MRAM/SOT-MRAM), and fault-tolerant computing. He actively publishes on topics like skyrmion logic gates and TRNG implementations using MRAM. His work bridges theoretical machine learning advancements with practical hardware implementations, addressing critical challenges in next-generation computing systems.

• Reliable Hardware Design
• Electronic Design Automation
• Edge Computing
• +۴ مورد دیگر

Dr. Abusaleh Jabir is a University Reader at the School of Engineering, Computing and Mathematics, Oxford Brookes University. He holds a DPhil in Computing from the University of Oxford and leads the Advanced Reliable Computer Systems (ARCoS) group. His research focuses on reliable hardware design, memristive nanotechnology, edge computing, and secure authentication systems. He has over 80 peer-reviewed publications and multiple patents, including innovations in error-tolerant circuits and memristive architectures. **Education**: DPhil in Computing (University of Oxford). **Research Interests**: Reliable hardware design, electronic design automation, sensing at the edge, physical uncloneable authentication, and emerging memristor technologies. His work addresses challenges in IoT, edge computing, and cybersecurity through innovative electronic systems. **Funding & Projects**: Current projects include the Leverhulme Trust-funded MONITOR gas sensor array initiative. His research has been supported by the UK Ministry of Defence, EPSRC, and Finance South East. **Awards & Patents**: Multiple patents granted, including EU 17706875.6 (memristive logic) and GB 1914221.5 (reconfigurable memristive logic). Recognized with best paper awards. **Advising & Impact**: Supervised PhD students now leading semiconductor and automotive industries (e.g., Infineon Technologies, Continental Teves AG). Collaborates with academic and industrial partners globally. **Labs & Teams**: Leads the ARCoS group within the Artificial Intelligence, Data Analysis and Systems (AIDAS) Institute, fostering interdisciplinary innovation in secure and reliable electronics.

• Low-power Integrated Circuits
• Very Large Scale Integration (VLSI) Circuit Design
• Sensors and Data Acquisition Systems
• +۸ مورد دیگر

Yusuf Leblebici is a Turkish academic and current President of Sabanci University (2018-present), reappointed in December 2022. He previously served as a Chair Professor and Director of the Microelectronic Systems Laboratory at EPFL, Switzerland (2002-2018), and held academic roles at Worcester Polytechnic Institute (1997-1999), Istanbul Technical University (1993-1997), and the University of Illinois at Urbana-Champaign (1991-2000). His career spans microelectronics, VLSI design, and neuromorphic systems. BSc and MSc in Electrical Engineering from Istanbul Technical University (1984, 1986) PhD in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign (1990) His research focuses on low-power integrated circuits , VLSI design , sensor interfaces , semiconductor modeling , and neuromorphic computing . He has co-authored over 400 scientific articles and seven books, including two internationally acclaimed textbooks. Scientific Awards : 2020 ECE Distinguished Alumni Award (UIUC) 2009 IEEE Fellow 2009 IEEE Distinguished Lecturer 1999 WPI Satin Distinguished Fellow Award 1995 Turkish TUBITAK Young Investigator Award He has graduated 58 PhD students and over 100 MSc students, and played a pivotal role in establishing Sabanci University's Microelectronics Program (1999-2002) and directing EPFL's Microelectronic Systems Laboratory (2002-2018). As Sabanci University President, he has enhanced global visibility, attracted international talent, and strengthened global collaborations.

Hongyu Anمشاهده پروفایل

استادیار

• Neuromorphic Computing
• Energy-efficient Neuromorphic Electronic Circuit Design
• Artificial Intelligence
• +۸ مورد دیگر

Hongyu An is an Assistant Professor in the Department of Electrical and Computer Engineering at Michigan Technological University. He holds affiliations with the Computer Science and Biomedical Engineering departments. Dr. An leads the BrainX Lab (Neuromorphic Robotics Lab and Neuromorphic Brain-Machine Interface Lab) and collaborates with the Institute of Computing and Cybersystems (ICC). He earned his PhD, MS, and BS in Electrical Engineering from Virginia Tech, Missouri University of Science and Technology, and Shenyang University of Technology respectively. Research Interests: Dr. An focuses on neuromorphic computing and its applications in AI hardware , robotics , and medical devices . His work spans memristor-based circuits , spiking neural networks , and energy-efficient AI systems . Key projects include associative learning in neuromorphic robots , neural prosthetics for memory restoration , and power-efficient adaptive deep brain stimulation systems . Publications & Research: With over 15 significant publications since 2016, Dr. An's work demonstrates expertise in 3D neuromorphic IC design , memristor reliability , and self-learning robotic systems . His research has appeared in journals like IEEE Transactions on Computing Aided Design and Frontiers in Computational Neuroscience. Awards & Funding: Bill and LaRue Blackwell Dissertation Award NSF CRII and ERI Awards USAF VFRP Fellowship Best Paper Nomination (2017 ISQED) Students & Collaborations: Dr. An mentors PhD students Tianze Liu and Md Abu Bakr Siddique, undergraduate Lucas Haddad, and volunteers like Vinay Kumar Pillalamarri. His team collaborates with Dr. Yan Zhang on neuromorphic brain-machine interfaces . The lab operates advanced infrastructure including LabLynx wireless neural recording systems and Intel Loihi-2 neuromorphic servers .